---
title: "Climate Legislature Visualizations"
author: "Daniel Palamarchuk"
output: 
  flexdashboard::flex_dashboard:
    source_code: embed
    theme: united
    
---

```{r global, echo=FALSE}
library(bslib)
library(flexdashboard)
library(plotly)
library(ggplot2)
library(plyr)
library(reactable)

source("functions.R")

csv17 <- read.csv("data/csv_2017.csv", nrows = 89) %>% col_care() %>% add_identifiers() %>% mutate(Year = 2017) %>%
    separate(Com.1, into = c("Com.1", "Com.1.2", "Com.1.3", "Com.1.4"), sep = ";")
csv18 <- read.csv("data/csv_2018.csv", nrows = 111) %>% col_care() %>% add_identifiers() %>% mutate(Year = 2018) %>%
    separate(Com.1, into = c("Com.1", "Com.1.2", "Com.1.3", "Com.1.4"), sep = ";")
csv19 <- read.csv("data/csv_2019.csv", nrows = 92) %>% col_care() %>% add_identifiers() %>% mutate(Year = 2019) %>%
    separate(Com.1, into = c("Com.1", "Com.1.2", "Com.1.3", "Com.1.4"), sep = ";")
csv20 <- read.csv("data/csv_2020.csv", nrows = 92) %>% col_care() %>% add_identifiers() %>% mutate(Year = 2020) %>%
    separate(Com.1, into = c("Com.1", "Com.1.2", "Com.1.3", "Com.1.4"), sep = ";")
csv21 <- read.csv("data/csv_2021.csv", nrows = 84) %>% col_care() %>% add_identifiers() %>% mutate(Year = 2021) %>%
    separate(Com.1, into = c("Com.1", "Com.1.2", "Com.1.3", "Com.1.4"), sep = ";")

csv_total <- rbind(csv17, csv18, csv19, csv20, csv21)

colors <- list("y2017" = "ef476f", "y2018" = "ffd166", "y2019" = "06d6a0", "y2020" = "118ab2", "y2021" = "073b4c",
               "dead" = "6a5d5d")
labs <- c("Introduced", "Committee 1", "Floor 1",
          "Committee 2", "Floor 2",
          "Delivered to Governor", "Signed by Governor", "Law", "Dead")
link_hover <- paste("Flow between %{source.label}
",
                   "and %{target.label}
",
                   "
%{value:.0f}")
node_hover <- paste("%{label}
",
                   "%{value:.0f}")
# Stuff for General Overview
joint_year <- rbind(
                    data_creator(csv17, colors$y2017),
                    data_creator(csv18, colors$y2018),
                    data_creator(csv19, colors$y2019),
                    data_creator(csv20, colors$y2020),
                    data_creator(csv21, colors$y2021)
                )
# Adds committees
csv_com_total <- separate(csv_total, Com.1, into = "Com.1", sep = ";") %>%
    mutate(Com.1 = if_else(Com.1 == "H-CL" | Com.1 == "H-LC", "H-CL/LC", Com.1),
    Com.2 = if_else(Com.2 == "H-CL" | Com.2 == "H-LC", "H-CL/LC", Com.2))
committees_total <- com_sierra(csv_com_total)
labels_total <- c("Introduced", levels(committees_total$x)[2:4], "Other Committee", "Passed Floor 1", gsub(".{2}$", "", levels(committees_total$x)[7:9]), "Other Committee", "Passed Floor 2", "Delivered to Governor", "Signed by Governor", "Passed")

com_17 <- com_sierra(csv17)
com_18 <- com_sierra(csv18)
com_19 <- com_sierra(csv19)
com_20 <- com_sierra(csv20)
com_21 <- com_sierra(csv21)
labels_17 <- c("Introduced", levels(com_17$x)[2:4], "Other Committee", "Passed Floor 1", gsub(".{2}$", "", levels(com_17$x)[7:9]), "Other Committee", "Passed Floor 2", "Delivered to Governor", "Signed by Governor", "Passed")
labels_18 <- c("Introduced", levels(com_18$x)[2:4], "Other Committee", "Passed Floor 1", gsub(".{2}$", "", levels(com_18$x)[7:9]), "Other Committee", "Passed Floor 2", "Delivered to Governor", "Signed by Governor", "Passed")
labels_19 <- c("Introduced", levels(com_19$x)[2:4], "Passed Floor 1",  "Passed Floor 1", gsub(".{2}$", "", levels(com_19$x)[7:9]), "Other Committee", "Passed Floor 2", "Delivered to Governor", "Signed by Governor", "Passed")
labels_20 <- c("Introduced", levels(com_20$x)[2:4], "Passed Floor 1",  "Passed Floor 1", gsub(".{2}$", "", levels(com_20$x)[7:9]), "Other Committee", "Passed Floor 2", "Delivered to Governor", "Signed by Governor", "Passed")
labels_21 <- c("Introduced", levels(com_21$x)[2:4], "Passed Floor 1",  "Passed Floor 1", gsub(".{2}$", "", levels(com_21$x)[7:9]), "Other Committee", "Passed Floor 2", "Delivered to Governor", "Signed by Governor", "Passed")
```


# General Overview {data-orientation=columns}

These graphs provide a general overview of the overall data in VA between 2017-2021.
The graph on the left shows how the different bills move through the legislative process by year.
The graph on the right shows the same bills, except color-coded based on Sierra Club's position on the bill.

Column
-------------------------------------

### Bill Lifetime per Year

```{r}

plot_ly(
  type = "sankey",
  arrangement = "snap",
  node = list(
    label = labs,
    x = c(0, 0.13, 0.2, 0.33, 0.5, 0.63, 0.81, 1, 1),
    y = c(0.5, 0.49, 0.20, 0.20, 0.20, 0.24, 0.17, 0.17, 0),
    color = "gray",
    hovertemplate = node_hover,
    pad = 10,
    thickness = 10),
  link = list(
    source = as.numeric(joint_year$x) - 1,
    target = as.numeric(joint_year$next_x) - 1,
    value = joint_year$n,
    color = ~as.factor(joint_year$color),
    hovertemplate = link_hover,
    line = list(color = "black", width = 0.5)
    )) %>%
  layout(title = "Annual Sankey with Joint Resolutions 2017-2021",
         xaxis = list(showgrid = F, zeroline = F),
         yaxis = list(showgrid = F, zeroline = F),
         showlegend = T)
```

Column
-------------------------------------

### Legend

```{r}
library(cowplot)
library(grid)
library(gridExtra)
library(ggpubr)
base_graph <- ggplot(csv_total, aes(Year, fill = as.character(Year))) + geom_bar() + 
  scale_fill_manual(
                     values = c(paste("#",colors$y2017, sep = ""),paste("#",colors$y2018, sep = ""),paste("#",colors$y2019, sep = ""),
                                paste("#",colors$y2020, sep = ""),paste("#",colors$y2021, sep = ""))) +
  theme_minimal() +
  labs(title = "Fate of Different Bills in Relation to Sierra Club's Position in 2017 and 2018",
       x = "Final Outcome of Bill", y = "Number of Bills",
       fill = "Year") + 
    theme(legend.text = element_text(size = 15),
          legend.title = element_text(size = 20),
          legend.key.size = unit(2, "cm"))
legend_year <- get_legend(base_graph)

gg17 <- ggplot(csv17, aes(Dis, fill = Pos)) + geom_bar() +
  scale_fill_manual(
                     values = c("Supported" = "#9ACD32", "Neutral" = "#B0E0E6", "Opposed" = "#FF4500")) +
  theme_minimal() +
  scale_x_discrete(limits = c("Died in Committee", "Died Elsewhere", "Passed into Law")) + 
  labs(title = "Fate of Different Bills in Relation to Sierra Club's Position in 2017 and 2018",
       x = "Final Outcome of Bill", y = "Number of Bills",
       fill = "Sierra Club Position") + 
    theme(legend.text = element_text(size = 15),
          legend.title = element_text(size = 20),
          legend.key.size = unit(2, "cm"))
legend_sierra <- get_legend(gg17)

ggarrange(legend_year, legend_sierra, ncol = 2)
```

### Sierra Club Sankey

```{r}
sierra_joint <- sierra_data(csv_total)

plot_ly(
  type = "sankey",
  arrangement = "snap",
  node = list(
    label = labs,
    x = c(0, 0.13, 0.2, 0.33, 0.5, 0.63, 0.81, 1, 1),
    y = c(0.5, 0.49, 0.20, 0.20, 0.20, 0.24, 0.17, 0.17, 0),
    color = "gray",
    hovertemplate = node_hover,
    pad = 10,
    thickness = 10), # 10 Pixel
  link = list(
    source = as.numeric(sierra_joint$x) - 1,
    target = as.numeric(sierra_joint$next_x) - 1,
    value = sierra_joint$n,
    color = ~as.factor(sierra_joint$color),
    hovertemplate = link_hover,
    line = list(color = "black", width = 0.5)
    )) %>%
  layout(title = "Sierra Sankey with Joint Resolutions 2017-2021",
         xaxis = list(showgrid = F, zeroline = F),
         yaxis = list(showgrid = F, zeroline = F),
         showlegend = T)
```

# 2017 {data-navmenu="Committee Analysis" data-orientation=rows}

Row
----------------------------------
### Graph

```{r}
plot_ly(
  type = "sankey",
  arrangement = "snap",
  node = list(
    label = labels_17,
    x = c(0, 0.2, 0.2, 0.2, 0.2),#, 0.35, 0.5, 0.65, 0.8, 1),
    y = c(0, -0.2, 0.20, 0.40, 0.8),#, 0.5, 0.5, 0.5, 0.5),
    color = "gray",
    hovertemplate = node_hover,
    pad = 10), # 10 Pixel
  link = list(
    source = as.numeric(com_17$x) - 1,
    target = as.numeric(com_17$next_x),
    value = com_17$n,
    color = ~as.factor(com_17$color),
    hovertemplate = link_hover,
    line = list(color = "black", width = 0.5)
    ))%>%
  layout(title = "Sierra by Committee in 2017",
         xaxis = list(showgrid = F, zeroline = F),
         yaxis = list(showgrid = F, zeroline = F),
         showlegend = T)
```
Row
---------------------------------

### Table

```{r}
rtable(csv17)
```

### Description

Here we see all the bills with various support levels separated by committee.
H-CL/LC (Committee on Commerce & Labor) is consistently the committee that introduces the most amount of bills to the legislative process, only losing the lead once H-ACNR (Agriculture, Chesapeake & Natural Resources) in 2019.

# 2018 {data-navmenu="Committee Analysis" data-orientation=rows}

Row
---------------------------------
### Graph

```{r}
plot_ly(
  type = "sankey",
  arrangement = "snap",
  node = list(
    label = labels_18,
    x = c(0, 0.2, 0.2, 0.2, 0.2),#, 0.35, 0.5, 0.65, 0.8, 1),
    y = c(0, -0.2, 0.20, 0.40, 0.8),#, 0.5, 0.5, 0.5, 0.5),
    color = "gray",
    hovertemplate = node_hover,
    pad = 10), # 10 Pixel
  link = list(
    source = as.numeric(com_18$x) - 1,
    target = as.numeric(com_18$next_x),
    value = com_18$n,
    color = ~as.factor(com_18$color),
    hovertemplate = link_hover,
    line = list(color = "black", width = 0.5)
    ))%>%
  layout(title = "Sierra by Committee in 2018",
         xaxis = list(showgrid = F, zeroline = F),
         yaxis = list(showgrid = F, zeroline = F),
         showlegend = T)
```
Row
---------------------------------
### Table

```{r}
rtable(csv18)
```
### Description

Here we see all the bills with various support levels separated by committee.
H-CL/LC (Committee on Commerce & Labor) is consistently the committee that introduces the most amount of bills to the legislative process, only losing the lead once H-ACNR (Agriculture, Chesapeake & Natural Resources) in 2019.

# 2019 {data-navmenu="Committee Analysis" data-orientation=rows}

Row
---------------------------------
### Graph

```{r}
plot_ly(
  type = "sankey",
  arrangement = "snap",
  node = list(
    label = labels_19,
    x = c(0, 0.2, 0.2, 0.2, 0.2),#, 0.35, 0.5, 0.65, 0.8, 1),
    y = c(0, -0.2, 0.20, 0.40, 0.8),#, 0.5, 0.5, 0.5, 0.5),
    color = "gray",
    hovertemplate = node_hover,
    pad = 10), # 10 Pixel
  link = list(
    source = as.numeric(com_19$x) - 1,
    target = as.numeric(com_19$next_x),
    value = com_19$n,
    color = ~as.factor(com_19$color),
    hovertemplate = link_hover,
    line = list(color = "black", width = 0.5)
    ))%>%
  layout(title = "Sierra by Committee in 2019",
         xaxis = list(showgrid = F, zeroline = F),
         yaxis = list(showgrid = F, zeroline = F),
         showlegend = T)
```

Row
---------------------------------
### Table

```{r}
rtable(csv19)
```
### Description

Here we see all the bills with various support levels separated by committee.
H-CL/LC (Committee on Commerce & Labor) is consistently the committee that introduces the most amount of bills to the legislative process, only losing the lead once H-ACNR (Agriculture, Chesapeake & Natural Resources) in 2019.

# 2020 {data-navmenu="Committee Analysis" data-orientation=rows}

Row
---------------------------------
### Graph

```{r}
plot_ly(
  type = "sankey",
  arrangement = "snap",
  node = list(
    label = labels_20,
    x = c(0, 0.2, 0.2, 0.2, 0.2),#, 0.35, 0.5, 0.65, 0.8, 1),
    y = c(0, -0.2, 0.20, 0.40, 0.8),#, 0.5, 0.5, 0.5, 0.5),
    color = "gray",
    hovertemplate = node_hover,
    pad = 10), # 10 Pixel
  link = list(
    source = as.numeric(com_20$x) - 1,
    target = as.numeric(com_20$next_x),
    value = com_20$n,
    color = ~as.factor(com_20$color),
    hovertemplate = link_hover,
    line = list(color = "black", width = 0.5)
    ))%>%
  layout(title = "Sierra by Committee in 2020",
         xaxis = list(showgrid = F, zeroline = F),
         yaxis = list(showgrid = F, zeroline = F),
         showlegend = T)
```

Row
---------------------------------
### Table

```{r}
rtable(csv20)
```
### Description

Here we see all the bills with various support levels separated by committee.
H-CL/LC (Committee on Commerce & Labor) is consistently the committee that introduces the most amount of bills to the legislative process, only losing the lead once H-ACNR (Agriculture, Chesapeake & Natural Resources) in 2019.

# 2021 {data-navmenu="Committee Analysis" data-orientation=rows}

Row
---------------------------------
### Graph

```{r}
plot_ly(
  type = "sankey",
  arrangement = "snap",
  node = list(
    label = labels_21,
    x = c(0, 0.2, 0.2, 0.2, 0.2),#, 0.35, 0.5, 0.65, 0.8, 1),
    y = c(0, -0.2, 0.20, 0.40, 0.8),#, 0.5, 0.5, 0.5, 0.5),
    color = "gray",
    hovertemplate = node_hover,
    pad = 10), # 10 Pixel
  link = list(
    source = as.numeric(com_21$x) - 1,
    target = as.numeric(com_21$next_x),
    value = com_21$n,
    color = ~as.factor(com_21$color),
    hovertemplate = link_hover,
    line = list(color = "black", width = 0.5)
    ))%>%
  layout(title = "Sierra by Committee in 2021",
         xaxis = list(showgrid = F, zeroline = F),
         yaxis = list(showgrid = F, zeroline = F),
         showlegend = T)
```

Row
---------------------------------
### Table

```{r}
rtable(csv21)

```
### Description

Here we see all the bills with various support levels separated by committee.
H-CL/LC (Committee on Commerce & Labor) is consistently the committee that introduces the most amount of bills to the legislative process, only losing the lead once H-ACNR (Agriculture, Chesapeake & Natural Resources) in 2019.

# Total {data-navmenu="Committee Analysis" data-orientation=rows}

Row
---------------------------------
### Graph

```{r}
plot_ly(
  type = "sankey",
  arrangement = "snap",
  node = list(
    label = labels_total,
    x = c(0, 0.2, 0.2, 0.2, 0.2),#, 0.35, 0.5, 0.65, 0.8, 1),
    y = c(0, -0.2, 0.20, 0.40, 0.8),#, 0.5, 0.5, 0.5, 0.5),
    color = "gray",
    hovertemplate = node_hover,
    pad = 10), # 10 Pixel
  link = list(
    source = as.numeric(committees_total$x) - 1,
    target = as.numeric(committees_total$next_x),
    value = committees_total$n,
    color = ~as.factor(committees_total$color),
    hovertemplate = link_hover,
    line = list(color = "black", width = 0.5)
    ))%>%
  layout(title = "Sierra by Committee in Total",
         xaxis = list(showgrid = F, zeroline = F),
         yaxis = list(showgrid = F, zeroline = F),
         showlegend = T)
```

Row
---------------------------------
### Table

```{r}
rtable(csv_com_total)
```

### Description

Here we see all the bills with various support levels separated by committee.
H-CL/LC (Committee on Commerce & Labor) is consistently the committee that introduces the most amount of bills to the legislative process, only losing the lead once H-ACNR (Agriculture, Chesapeake & Natural Resources) in 2019.

# Other States

## Description

This page is under construction and will be updated as more states are scoured and added.